A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation

• Interval type-2 fuzzy set is used instead of type-1 fuzzy set in fuzzy clustering.
• A spatial membership is proposed for better performance in image segmentation.
• The validity functions are modified for estimating the algorithms.
• Experimental comparisons of four algorithms on MR images are given.

The fuzzy C-means (FCM) algorithm has significant importance compared to other methods in Medical image segmentation. Conventional FCM algorithm is sensitive to noise especially in the presence of intensity inhomogeneity in MRI. Main reason is that a single fuzzifier in FCM cannot properly represent pattern memberships for all clusters. In this paper, we present a novel algorithm for fuzzy segmentation of MRI data. The algorithm utilizes two fuzzifiers used in interval type-2 FCM and a spatial constraint on the membership functions. Also, in our investigation, validity functions are extended to generalized form for interval type-2 fuzzy clustering. The experimental results on both synthetic and MR images show that the proposed algorithm has better performance on image segmentation than conventional FCM based algorithms.